ECOS is an open-source numerical software package for solving optimization problems with second-order cone constraints (SOCPs). This includes linear (LPs), quadratic (QPs), and quadratically-constrained quadratic programs (QCQPs). ECOS also supports a small number of binary or integer variables by employing a simple branch and bound technique. ECOS is written entirely in ANSI C and does not depend on dedicated libraries for the required linear algebra computations operating on the (sparse) problem data. As a consequence, it can be used to solve optimization problems on any embedded system for which a C-compiler is available. The implemented solution algorithm is an interior-point method that is an efficient standard algorithm for solving convex optimization problems. It uses regularization and iterative refinement techniques to be numerically robust. The solution methods have been developed in cooperation with Prof. Stephen Boyd of Stanford University. A number of helpful contributors have provided interfaces to the following programming and modeling languages: CVX (Michael Grant), YALMIP (Johan Löfberg), Julia (João Felipe Santos, Iain Dunning, Anthony Kelman)

References in zbMATH (referenced in 14 articles )

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  1. Jarre, Florian; Lieder, Felix: The solution of Euclidean norm trust region SQP subproblems via second-order cone programs: an overview and elementary introduction (2018)
  2. Amir Ali Ahmadi, Anirudha Majumdar: DSOS and SDSOS Optimization: More Tractable Alternatives to Sum of Squares and Semidefinite Optimization (2017) arXiv
  3. Anqi Fu, Balasubramanian Narasimhan, Stephen Boyd: CVXR: An R Package for Disciplined Convex Optimization (2017) arXiv
  4. Hallac, David; Wong, Christopher; Diamond, Steven; Sharang, Abhijit; Sosič, Rok; Boyd, Stephen; Leskovec, Jure: SnapVX: a network-based convex optimization solver (2017)
  5. Ahmadi, Amir Ali; Majumdar, Anirudha: Some applications of polynomial optimization in operations research and real-time decision making (2016)
  6. Diamond, Steven; Boyd, Stephen: Matrix-free convex optimization modeling (2016)
  7. Diamond, Steven; Boyd, Stephen: CVXPY: a Python-embedded modeling language for convex optimization (2016)
  8. Kariotoglou, Nikolaos; Margellos, Kostas; Lygeros, John: On the computational complexity and generalization properties of multi-stage and stage-wise coupled scenario programs (2016)
  9. Lipp, Thomas; Boyd, Stephen: Variations and extension of the convex-concave procedure (2016)
  10. Liu, Xinfu; Shen, Zuojun: Rapid smooth entry trajectory planning for high lift/drag hypersonic glide vehicles (2016)
  11. David Hallac, Christopher Wong, Steven Diamond, Abhijit Sharang, Rok Sosic, Stephen Boyd, Jure Leskovec: SnapVX: A Network-Based Convex Optimization Solver (2015) arXiv
  12. Moehle, Nicholas; Boyd, Stephen: A perspective-based convex relaxation for switched-affine optimal control (2015)
  13. Harris, Matthew W.; Açıkmeşe, Behçet: Maximum divert for planetary landing using convex optimization (2014)
  14. Mayne, David Q.: Model predictive control: recent developments and future promise (2014)